Today's trend in media broadcasting/streaming is that the transport channels become more and more heterogeneous. Content providers and broadcasters continuously loose control of parts of the distribution chain. Thus, at the time of encoding audio content it may not be known at which data rate the content can be delivered to the customer.
The following solutions have been proposed or are used to tackle the problem today.
Usually, the content is encoded with a data rate that is corresponding to a worst-case transmission scenario. That is, the data rate is specified such that it reflects the maximum possible data rate expected to be deliverable to all of the customers. This has the disadvantage that most of the customers suffer from quality degradation although the transmission capacities would be better than worst case for them.
A better solution is to provide the same content at a selection of different data rates, i.e. several streams of the same content, each encoded with a different data rate.
Thus, the customer can select the version matching the specific quality demand and channel capacity, as used e.g. in Internet streaming. However, a significant part of the data is identical in each channel, so that much bandwidth is required for transmitting redundant data. As another drawback, the customer or decoder has to find and select the channel with the applicable data rate.
Another option is to apply transcoding of the content within the transmission chain. One example is to encode the content with a rather high data rate in a first step, and apply transcoding techniques at a later time if the data rate exceeds the actual transmission capacity. However, transcoding usually requires decoding and re-encoding, and leads therefore often to data quality degradation, e.g. by distortion, that is inherent to encoding and decoding processes. The quality degradation caused by this kind of transcoding comes additional to the quality degradation of the initial encoding. Further, these processes are computationally complex and require significant processing power at the points of transcoding.
In some solutions, there is a feedback from the customer to the encoding process, e.g. in adaptive multi-rate (AMR) speech coding. For practical broadcasting applications this approach is unusable since feedback control loops cannot be extended to a large number of users. Moreover, the feedback controls the complete, computationally complex encoding process, which is disadvantageous for off-line content, as e.g. in non real-time transmission.
Other solutions use bit stream scalability, like e.g. MPEG-4 Scalable-to-Lossless (SLS). Though current scalable approaches are specifically tailored for the targeted scenarios, they are in general not backwards compatible to previous standards, so that the customer needs a specific decoder to exploit the scalable portion of the signal. A conventional decoder can decode only the basic part of the signal, e.g. of an MPEG SLS data stream. Further, there is a quality penalty owing to the scalable bit stream format at least for some of today's scalable approaches. In particular, for the popular MPEG-1 Layer III (mp3) format no scalable approach is known.